Chemical Engineering Commons^™

Expansion Of The Functionality Of Discrete Fiber-Optic Liquid Level Sensors, Azimjon Mamadaliyevich Khusanov, Yuriy Gennadyevich Shipulin

Chemical Technology, Control and Management

The article examines the problems and prospects of creating discrete fiber-optic liquid level gauges. Functional diagrams of single-channel and multi-channel discrete fiber-optic liquid level meters are presented. Examining the challenges of keeping this system running continuously reveals that sustaining its required reliability necessitates replacing malfunctioning measurement systems with operational ones. Investigations into differential equation models demonstrate that system recovery and failure events occur independently and follow a Poisson distribution pattern.

Investigation Of Nonlinear Magnetic Circuits Of Measuring Transducers With A Special Parameter Distribution Structure, Javhar Sulton O'G'Li Fayzullayev

Chemical Technology, Control and Management

The article proposes a new analytical method for investigating nonlinear magnetic circuits with a special structure of parameter distribution. The method is based on introducing into the system of nonlinear differential equations of such circuits the condition that the second derivative of the magnetic flux along the length of the circuit is equal to zero, as well as on the assumption that one of the geometric parameters of the studied magnetic circuit – the size of the air gap between the ferromagnetic rods, their thickness, width, or the linear value of the number of turns of the distributed excitation winding …

Empirical Analysis Of Machine Learning Models For Predicting Equipment Failures Using Iot Sensor Data, Yusuf Shodiyevich Avazov

Chemical Technology, Control and Management

This article examines the problem of detecting and predicting industrial equipment faults using IoT sensor data through machine learning techniques. Sensor readings such as temperature, vibration, pressure, voltage, and current, as well as FFT-based features, were statistically analyzed. Class imbalance and low signal informativeness were identified as key factors limiting model accuracy. Results obtained from Logistic Regression, Random Forest, and XGBoost models were comparatively evaluated, showing that when ROC-AUC values remain around 0.5, distinguishing fault and non-fault states becomes challenging. Correlation and feature-importance analyses confirmed the absence of strong dominant indicators. The findings highlight the need to improve sensor architecture …

Current Trends In Industrial Gas Furnace Control And Monitoring Systems, Utkir Uktam O'Gli Kholmanov

Chemical Technology, Control and Management

This paper presents a critical review of modern control methods applied to industrial gas-fired furnaces. Conventional PID controllers and their advanced modifications, including Advanced Process Control (APC) and cascade control schemes, are analyzed alongside Model Predictive Control (MPC) approaches. In addition, intelligent control algorithms—such as fuzzy logic and neuro-fuzzy systems (Fuzzy, ANFIS)—are examined. The study also considers advanced sensing technologies, including Tunable Diode Laser Absorption Spectroscopy (TDLAS), acoustic pyrometry, and infrared pyrometers, as well as digital twins and CFD-based modeling techniques. Particular attention is given to a comparative evaluation of control strategies based on key performance criteria, including energy efficiency, …

Skeleton-Based Human Action Recognition Using Spatio-Temporal Latent Features With A Gcn Model, Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Chemical Technology, Control and Management

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates …

Analysis Of Leakage In Nearshore Pipelines Using Fitness For Service Method To Ensure Mechanical Integrity Based On Api-579 Level 3, Elriandri Elriandri, Dedi Priadi

Journal of Materials Exploration and Findings

The Fitness for Service (FFS) analysis is performed as a quantitative assessment to evaluate the integrity condition of a pipeline. Essentially, FFS assessment helps determine whether equipment components can operate safely despite existing deficiencies. This evaluation is carried out using the Finite Element Method (FEM). In the case of an underwater pipeline that experiences leak due to anchor pull at the flange connection, it undergoes plastic deformation and is lifted approximately 1 meter. As a mitigation step, inspection and repairs have been carried out by the company. Subsequently, modeling is performed to reconstruct the deformation process of the pipeline. Then, …

Assessment Of The Melt Quality Of A 30% Scrap Adc12 Aluminium Alloy Using The Inclusion And Fluidity Measurement Instrument (Ifmi) With Mullite Ceramic Filters, Gusti Ruri Ruri Lestari, Sandya Ananda Riswan, Muhammad Anis, Ahmad Ashari, Paramita Vidya Ayuningtyas, Bambang Suharno Prof, Donanta Dhaneswara

Journal of Materials Exploration and Findings

The increasing demand for sustainable practices in the metal casting industry has driven the use of recycled aluminum alloys such as ADC12. However, the addition of aluminum scrap tends to increase oxide inclusions, which reduce melt fluidity and compromise casting quality. This study utilizes the Inclusion and Fluidity Measurement Instrument (IFMI), equipped with mullite ceramic filters, to assess the melt quality of ADC12 aluminum alloy containing 30% scrap. Fluidity and inclusion characteristics were evaluated at five pouring temperatures (660°C, 680°C, 700°C, 720°C, and 740°C). The results show that fluidity increased with temperature, reaching a peak of 84.6 g·s⁻¹ at 740°C, …

Equipment Criticality Analysis To Determine Asset Integrity Management System Scheme In Supporting Production Optimization Scenarios In The Aeging Field, Donny Andryanto, Donanta Dhaneswara

Journal of Materials Exploration and Findings

This research investigates the production decline and cost increase in the X&Y oil and gas fields from 2022 to 2023. Production fell by 34%, while production costs per barrel rose by 79%. To address these issues, a series of optimization processes are proposed. These aim to restructure and enhance the production facilities to reduce current production costs. The optimizations include reducing pressure at the PPP Platform in 2024. Additional steps include shutting down CPP-ORF 14” pipelines and most processes at CPP 2 Platform by 2027 to convert it into an accommodation platform. Further, the plan involves optimizing the release of …

Morphological Test Of Areca Nut Fiber Ceramic Membrane Using Scanning Electron Microscopy Energy Dispersive X-Ray Mapping Spectroscopy, Amelia Marzain, Siti Umi Kalsum, Marhadi Marhadi, Ahmad Nabil Shahab

Journal of Materials Exploration and Findings

This study investigates the potential of ceramic membranes derived from areca nut fiber as a cost-effective and environmentally sustainable material for the removal of iron (Fe) and manganese (Mn) from groundwater. Two types of membranes were fabricated: one without activation and one chemically activated using 10% sodium hydroxide (NaOH). The morphological and elemental characteristics of both membranes were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) mapping. The concentrations of Fe and Mn before and after treatment were measured using Atomic Absorption Spectroscopy (AAS). The NaOH-activated membrane exhibited a more porous surface structure and higher oxygen content, …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Physicochemical Analysis Of Carbon-Containing Materials Obtained From Vulcanized Rubber Waste And Their Application As Filler Pigments, N.S. Baxranova, Sh.T. Jurayev, D.R. Muminova, Jurayev Shohruh Tulqinovich

Chemical Technology, Control and Management

The article presents the results of chemical and scanning electron microscopic analyses of the solid fraction of carbon-containing materials obtained through thermo-oxidative pyrolysis of vulcanized rubber waste. Elemental analysis of the solid product formed during high-temperature pyrolysis of rubber-technical products at 650–700 °C is provided. In addition, the potential use of the carbonaceous material as a pigment is examined. Raman spectroscopic analysis of worn automobile tires showed that their composition consists of approximately 80 wt.% C, 7 wt.% H, 0.4 wt.% N, 1.5 wt.% S, 3 wt.% O, and 8 wt.% inorganic substances.

Analysis Of A Cloud-Based Robot Motion Planning System, Yusif Mardanzade, Latafat Abbas Gardashova

Chemical Technology, Control and Management

As a result of the integration of cloud computing technologies into the field of robotics, the concept of "cloud robotics" has emerged. Unlike traditional robots, cloud-based robot systems remove computation, memory, and even some software from the local device and rely on remote resources obtained over the network. This approach ensures that robots are not limited only by their internal computing capabilities and allows them to take advantage of the wide range of opportunities offered by the cloud infrastructure. As a result, robots have access to large databases, highly parallel computing, and collective learning capabilities anytime and anywhere. In addition, …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

The Use Of Diagnostic And Restructuring Methods To Build Reliable Management Systems, Khurshid Salim Ugli Turayev

Chemical Technology, Control and Management

This article is devoted to the applied analysis of diagnostic and restructuring methods aimed at ensuring the reliability of control systems in the event of failures. The paper considers practical implementations of diagnostic and control algorithms using a servo drive setup as an example. The results of experiments are presented, demonstrating the system's behavior under various fault conditions. A comparative analysis of the effectiveness of the proposed solutions is carried out in terms of stability and operational accuracy. The obtained data confirm the feasibility of using adaptive control structures to increase the fault tolerance of technical systems. The article concludes …

Modern Significance And Development Trends In The Production Of Vegetable Oils, Umidjon Ruziev, F.O. Qosimov, M.K. Shodiev

Chemical Technology, Control and Management

This article provides a comprehensive analytical review of the current state of the global vegetable oil production market. It describes the diversity of raw materials supplied, which includes both traditional and emerging fat sources. The paper also describes the technological stages of production, emphasizing modern innovations that contribute to more efficient, high-quality, and safe production. The article also covers global trends by comparing production dynamics between countries, highlighting regions with the highest rates of production growth and explaining the reasons for their competitive advantages. Particular attention is paid to environmental and socio-economic aspects, including sustainable land use, certification, carbon footprint, …

Investigation Of The Fuel Combustion Process In Gas-Fired Furnaces For Automation Systems, N.R. Yusupbekov, Sh.M. Gulyamov, A.T. Rajabov, U.U. Kholmanov

Chemical Technology, Control and Management

The regularities of the combustion process of gaseous fuel in chamber furnaces are described. This process represents a homogeneous reaction in which there is no distinct boundary surface between the fuel and the oxidizer. It is shown that the latter either mix and then burn subsequently, or both processes occur simultaneously, corresponding respectively to kinetic and diffusion combustion. The structure of a turbulent-diffusion flame of gaseous fuel combustion is presented.

Development Of Fire Prediction And Prevention Digital System Algorithms, Oybek Zokirovich Koraboshev

Chemical Technology, Control and Management

This research work is devoted to the development of algorithms for a digital system aimed at early detection, prediction and prevention of fire hazards. In the work, the process of fire hazard assessment is modeled on the basis of modern information technologies and artificial intelligence tools. The main focus is on collecting data in real time, analyzing it and creating algorithms that determine the level of danger. In the process of research, methods of data cleaning, normalization and determination of correlation between variables were used to process multidimensional data streams obtained from various sensors (temperature, smoke, gas concentration and humidity …

Algorithms For The Synthesis Of A Temperature Control System For The Inner Tube Heat Exchanger With A Steam Jacket, H.Z. Igamberdiyev, Jasur Sevinov, U.F. Mamirov, Sh.M. Abdishukurov

Chemical Technology, Control and Management

The synthesis of a feedback propagation control law for an inner tube heat exchanger with a steam jacket is addressed in this text. A controller has been developed that, based on temperature measurements taken at four points. The maintains the output temperature at a specified level by acting on the steam jacket temperature. To determine the parameters of the plant, a linear quadratic optimal (LQ-optimal) algorithm is employed. In the considered case, the optimal controller includes a proportional–integral (PI) component, as well as an additional term that requires storing the control input over the current interval for its computation. The …

Intelligent Method Of Dynamic Control For A Class Of Stochastic Nonlinear Systems, Isamidin Khakimovich Siddikov, Davronbek Abdalimovich Khalmatov, Gulchekhra Rakhimjanovna Alimova, Dilnoza Rakhmanovna Khushnazarova

Chemical Technology, Control and Management

The paper considered the problems of researching the stabilisation system and backstepping control of stochastic nonlinear systems. The characteristics of stochastic nonlinear dynamic control systems are random signals with normal lawful distribution, which significantly complicates task control. In stochastic control, it is necessary to determine the trajectories of the control variables in order to achieve the desired control objective at minimum cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this work, a neuro-fuzzy network is used to evaluate the parameters of the control system with backstepping, …

Reinforcement Learning In A Virtual World: A Study Of Ppo And Sac Within Unity Ml Agents, Rufat Mammadzada

Chemical Technology, Control and Management

This study explores the use of Unity3D as a versatile platform for developing, training, and evaluating intelligent agents through reinforcement learning. Leveraging the Unity ML-Agents Toolkit, a dynamic 3D environment was created to examine agent learning behavior using two advanced algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The simulation environment consisted of navigable terrain bounded by red borders, with collectible blue balls serving as rewards and a purple cube representing the agent. A carefully designed reward system was implemented to encourage goal-directed behavior and penalize inefficiency, while time constraints introduced an additional challenge requiring both precision and speed. …

Condition Optimization For The Synthesis Of Castor Oil Templated Mesoporous Silica, Godlisten Namwel Shao

Tanzania Journal of Engineering and Technology (TJET)

The present work provides suitable conditions for the synthesis of mesoporous materials with improved porosity using an optimization technique. The proposed preparation method involves forming final products by varying the amount of castor oil, media, and template removal. The obtained samples were examined by TGA, XRD, DRIFT and nitrogen physisorption studies. It was observed that the porosity of the obtained samples was dependent on the conditions under which the materials were synthesized. All synthesized materials showed the Type IV adsorption-desorption isotherm features of mesoporous, regardless of the conditions utilized during the synthesis. The sample synthesized using 2.5 g of a …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Increasing The Robustness Of A Control System For A Complex Dynamic Plant By Correcting Nonlinearity In The Warping Process, Tukhtamurod Khayitmurodovich Avezov, Zokhid Ergashboyevich Iskandarov

Chemical Technology, Control and Management

The paper discusses the challenges of enhancing the robustness of a control system for a complex dynamic plant by addressing nonlinearity in the warping process. Devices that ensure the stability of the control system against parameter non-stationarity on the warping machine are referred to as state controllers. The operating principle of these devices relies on providing artificial nonlinearity to the rear connection circuit of the control system's actuator. However, this nonlinearity is implemented using components that consider the parameters of low control quality. Therefore, it is necessary to continuously adjust the nonlinearity parameters to, on one hand, reduce the load …

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Modern Methods Of Self-Monitoring, Diagnostics, And Fault Tolerance In Flow Measurement Systems, Elbek Ortiqov

Chemical Technology, Control and Management

This article investigates various methods and tools for self-monitoring and fault tolerance in flow measurement transducers used in industrial processes. The study focuses on key techniques such as the use of redundancy, generation of reference values, analysis of measurement signals, and control of disturbance variables. These methods allow transducers to detect potential faults, ensure reliable operation, and maintain measurement accuracy even under adverse conditions. The article highlights how self-monitoring contributes to improving system safety, increasing reliability, and reducing downtime. It also discusses the integration of intelligent monitoring systems that support predictive maintenance and real-time diagnostics. Fault-tolerant sensors with self-monitoring capabilities …

Finding The Shortest And Optimal Path With Metaheuristic And Madm Methods, Narmin Ibrahim Hasanli

Chemical Technology, Control and Management

The article had described a method for finding the shortest and most optimal path among cities. The data had been taken from the TSPLIB library, which had provided standard examples for the Traveling Salesman Problem. This approach had integrated the advantages of the meta-heuristic technique and the Multi Attribute Decision Making method to solve the problem effectively. In the first stage, the population based meta-heuristic method ACO (Ant Colony Optimization) had found optimal solutions in large search spaces. The use of pheromone trails, heuristic information and an iterative search process had given the opportunity to find the best or near-best …

Modeling Of Urea Drying And Granulation Process In Fluidized Bed, Jalolitdin Pakhritdinovich Mukhitdinov, Aleksey Viktorovich Schulz

Chemical Technology, Control and Management

This article is devoted to the mathematical modeling of urea drying and granulation processes in a fluidized bed. A brief description is provided for the functional blocks included in the mathematical model, along with the required parameters that form an integrated representation of the technological process. The SR-POLAR model is used to describe a phase equilibrium between the components involved. The interconnections between functional blocks are shown in the process flow diagram. Block diagrams for modeling a multi-chamber granulation unit and the cooling system for the resulting granules are presented. Granule growth in the fluidized bed is described using a …

Algorithms For Assessing Soil Salinity Levels Based On Remote Sensing Imagery, Bobomurod Mamitjonovich Tojiboev

Chemical Technology, Control and Management

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information …